self-recruitment, connectivity, and larval export in mediterranean

SELF-RECRUITMENT, CONNECTIVITY, AND LARVAL EXPORT IN MEDITERRANEAN MARINE PROTECTED AREAS: A MODELLING APPROACH FOR THE DUSKY GROUPER
EPINEPHELUS MARGINATUS.
Marco ANDRELLO1,2, David MOUILLOT3,4, Jonathan BEUVIER5,6, Camille ALBOUY3,7, Wilfried THUILLER2 and Stéphanie MANEL1.
1 Laboratoire Population Environnement et Développement, UMR-IRD 151, Université Aix-Marseille, Marseille, 13331, France. 2 Laboratoire d'Ecologie Alpine, CNRS UMR 5553, Université Joseph Fourier, Grenoble 1, BP 53, 38041 Grenoble Cedex 09, France. 3 UMR CNRS-UM2-IRD-IFREMER 5119 ECOSYM, Université Montpellier 2 CC 093, 34095
Montpellier Cedex 5, France. 4 ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Qld 4811, Australia. 5 Mercator Océan, Parc Technologique du Canal, 8-10 rue Hermès, 31520 Ramonville St-Agne, France. 6 Groupe d’Etude de l’Atmosphère Météorologique, Centre National de Recherches Météorologiques, MétéoFrance/CNRS, Toulouse, France. 7 IRD, UMR 212 EME, CRHMT, Avenue Jean Monnet 34203 Sète Cedex, BP 171, France
1. Introduction
2. Materials and methods
Three-dimensional sea current velocities were provided by the NEMOMED12 model (Beuvier et al. 2012).
The model has a horizontal resolution of 1/12th degree, corresponding to a 6-8 km horizontal cell width. The
model is forced with the atmospheric wind stress, total and solar heat fluxes, total freshwater flux
(evaporation minus precipitation), and river discharge (33 main rivers mouths plus a coastal runoff) and
returns the three-dimensional velocity fields for years 2004-2008.
Passive larval dispersion was simulated using the software Ichthyop 3.1 (Lett et al. 2008). 1000 larvae were
virtually released on the 1st of August at 20 cm depth in each MPA each year, according to observations of
spawning behaviour in the sea (Zabala et al. 1997). Pelagic larval duration (PLD) was set to 30 days (Cunha
et al. 2009; Macpherson & Raventos 1997).
The effectiveness of marine protected areas (MPAs) for protecting exploited populations depends on larval dispersal distances, self-recruitment, connectivity and larval export to unprotected areas:
1. Dispersal distance of larvae must be greater than distance between MPAs to allow for connectivity
2. Self-recruitment is the fraction of larvae settling back into their natal population; it controls local population growth and persistence.
3. Connectivity is the exchange of individuals among MPAs in the form of larval dispersal, determining population persistence and growth at the metapopulation scale.
4. Larval export to unprotected areas allows MPAs to be employed as fisheries management tools, because they can replenish fish stocks
Here, we implemented a biophysical model to estimate larval dispersal distances, self-recruitment within Mediterranean MPAs, connectivity among them, and larval export to harvested areas. The model
was parameterized according to the dispersal capacity of the dusky grouper Epinephelus marginatus, an overexploited species that needs protection from both recreational and commercial fisheries:
3.1 Results: Geographic distances
(A) Distances between all pairs of MPAs
(B) Nearest neighbour distances between MPAs
3.3 Connectivity
(C) Mean and (D) maximal distances of larval transport for
each MPA.
Connectivity matrix. Colours represent the probabilities that a larva
born in MPA j (column) is transported to MPA i (line). MPAs are first
sorted by ecoregion, then alphabetically within ecoregions. ALB, Alboran
Sea; WEST, Western Mediterranean; TUS, Tunisian Plateau and Gulf of
Sidra; ION, Ionian Sea; ADR, Adriatic Sea; AEG, Aegean Sea; LEV,
Levantine Sea.
3.2 Self-recruitment
Connectivity between MPAs: probability that a larva moves between two MPA
(excluding mortality). Connectivity was stronger in areas with a high density of
MPAs, such as the Balearic Islands, the Gulf of Trieste, the Central Dalmatian
Islands and the Southern Turkish coast.
3.4 Larval export
53% of larvae were lost in the open sea (>200 m).
Self-recruitment was highly variable across MPAs (range: 0 - 39%) and
increased with MPA size.
Self-recruitment
0.1
Linear regression, F1,98 = 110.4, P < 0.0001, intercept (estimate ± standard
error) -3.53 ± 0.12, slope 0.79 ± 0.08, R2 = 0.53. Values are log10-transformed
after excluding null values. The regression statistics imply that self-recruitment
increases at a rate of approximately 0.2% per km2.
0.01
0.001
47% of larvae settled in coastal waters (depth <200 m)
• 8% settled in MPAs
• Regions accumulating larvae (yellow and red) are located near large MPAs
and in areas where the continental shelf extends far offshore (e.g. the Northern
Adriatic Sea).
• Larval export is very poor (green and blue) or totally absent (grey) over the
vast majority of the continental shelf.
• The entire Southern Mediterranean coast, especially the South-eastern
coast, does not benefit from larval export.
0.0001
1
10
100
1000
MPA size (square kilometres)
4. Conclusions
We have used a biophysical model to evaluate larval dispersal distance, self-recruitment, connectivity and larval export for the marine protected areas (MPAs) of the Mediterranean Sea.
The estimates of self-recruitment and connectivity derived here will be useful to analyse the viability of E. marginatus populations in the Mediterranean Sea in conjunction with demographic data on adult fishes.
Our study also poses new challenges for the design of a future Mediterranean MPA network according to the Strategic Plan for Biodiversity adopted by the Convention on Biological Diversity (COP10;
www.cbd.int/cop10), with at least 10% of coastal and marine areas protected by 2020. This network will need to be a “true” network that ensures connectivity within and among MPAs, in order to supply larvae all
over the Mediterranean continental shelf. Its optimal design will also need to take into account the economic and social costs deriving from limitations on fishing activities as well as global climate change, which is
likely to modify larval dispersal through variation in pelagic larval duration (PLD) and currents.
References
Beuvier et al. 2012. Journal of Geophysical Research, 117 (C07022).
Cunha et al. 2009. Scientia Marina 73S1, 201-212.
Lett et al. 2008. Environmental Modelling & Software 23, 1210-1214.
Macpherson & Raventos. 2006. Marine Ecology-Progress Series 327, 257-265.
Zabala et al. 1997. Scientia Marina 61, 65-77.